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KDD 2014 Research Track • 1036 submissions from 2600 authors – 42% increase over KDD ’13 • 151 papers: – Acceptance rate 14.6% 0 200 400 600 800 1000 1200 2000 2005 2010 2015 KDD year Numberofsubmissions 5. As one of the world’s top international conference in data mining, KDD is known for a strict paper review process that yields an annual acceptance rate of no more than 20 percent. Conference acceptance rates. (16.8% acceptance rate) KDD, 2016. [11] Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu and Mark Coates, “Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation”, in the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020 Research Track, acceptance rate: 216/1279=16.9%), San Diego, USA, Aug. 2020. Sign Up Now. All papers published in the proceedings are peer reviewed by a committee of international researchers in data mining, often with an acceptance rate of less than 30%. Registration. 2.1 Main session; 2.2 Short papers; 3 NAACL HLT. Data mining and deep learning researcher working primarily with graphs Fighting Opinion Control in Social Networks via Link Recommendation, Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging, Focused Context Balancing for Robust Offline Policy Evaluation, GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorzation, Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space, Graph Convolutional Networks with EigenPooling, Graph Recurrent Networks with Attributed Random Walks, Graph Representation Learning via Hard and Channel-Wise Attention Networks, Graph Transformation Policy Network for Chemical Reaction Prediction, Graph-based Semi-Supervised & Active Learning for Edge Flows, GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data, HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings, HetGNN: Heterogeneous Graph Neural Network, Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications, Hidden POI Ranking with Spatial Crowdsourcing, Hierarchical Gating Networks for Sequential Recommendation, Hierarchical Multi-Task Word Embedding Learning for Medical Synonym Prediction, Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts, Identifiability of Cause and Effect using Regularized Regression, Improving the quality of explanations with local embedding perturbations, Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis, Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding, Interpretable and Steerable Sequence Learning via Prototypes, Interview Choice Reveals Your Preference on the Market:To Improve Job-Resume Matching through Profiling Memories, Investigating Cognitive Effects in Session-level Search User Satisfaction, Is a Single Vector Enough? Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^. KDD 2014 Research Track • 1036 submissions from 2600 authors – 42% increase over KDD ’13 • 151 papers: – Acceptance rate 14.6% 0 200 400 600 800 1000 1200 2000 2005 2010 2015 KDD year Numberofsubmissions 5. KDD 2019, Research Track (acceptance rate: 14.0%) Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is Acceptance rate: 26.5%. KDD-2013 had about 1,200 attendees, which makes it the largest research, peer-reviewed conference in Data Mining, Data Science, and Knowledge Discovery, ever (so far). S. Yoon, K. Park, J. Shin, H. Lim, S. Won, M. Cha, K. Jung [In proc. We'll be updating the website as information becomes available. KDD Reviewing Process 46 Senior PC members + 340 PC members • 2971 reviews in total (Rough) Acceptance rule: • Raw review score AND Standardized review score AND … [[10] Jianing Sun, Yingxue Zhang, Wei Guo, … The proceedings of each PAKDD conference are published by Springer-Verlag as part of the Lecture Notes in Artificial Intelligence series. (acceptance rate 21%) PDF . He, and Y. Zhu. KDD'14: 14.6% (151/1036)-KDD'15: 19.5% (160/819)-KDD'16: 13.7% (142/1115)-KDD'17: 17.4% (130/748)-KDD'18: 18.4% (181/983) (107 orals and 74 posters)-KDD'19: 14.2% (170/1200) (110 … KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11% Overall Acceptance Rate 1,907 of 12,895 submissions, 15% Here is a list of some acceptance rates of Theoretical Computer Science (TCS) Conferences (and some of computational biology). KDD 2020. The primary emphasis is on papers that either solve or advance the understanding of issues related to deploying data science technologies in the real world. KDD 2021 -ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 04/2019: 1 long paper about knowledge extraction from text has been accepted by KDD 2019 (Research Track, Acceptance Rate: 14.2%, Oral) 08/2018: 1 paper … This year’s acceptance rate for an oral presentation was below 6%. considered to be more practical and more related with real-world applications. A Simple and General Graph Neural Network with Stochastic Message Passing. Nearly 2600 people have registered so far. 205-214, San Francisco, California, Aug 2016. You can always update your selection by clicking Cookie Preferences at the bottom of the page. PDF Code Dataset Video Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning. KDD continues to be the leading research conference in the field, and this year received 726 papers, from which only 125 were accepted, 17.2% acceptance ratio. ICIBM 2020. they're used to log you in. (Acceptance rate: 85/396=21.5%, applied data science track) Fei Wu*, Pranay Anchuri, and Zhenhui Li, Structural Event Detection from Log Messages, in Proceedings of the 2017 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17), Halifax, Nova Scotia, Aug. 2017. Chapter participation provides a unique combination of social interaction and professional dialogue among peers. How to do good research, Get it published in SIGKDD and get it cited! 35+ journal publications. Some good examples include recommender systems, clustering, graph mining, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering: (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF You signed in with another tab or window. Submitted papers will go through a peer review process. Send this CFP to us by mail: cfp@ourglocal.org. Typical acceptance rates are in the 15%-30% range depending on the year, the location and the particular conference. Exploring Sydney >>Chinese Version. Fates of Microscopic Social Ecosystems: Keep Alive or Dead? KDD 2015 will be the first Australian edition of KDD, and is its second time in the Asia Pacific region. Background. If nothing happens, download the GitHub extension for Visual Studio and try again. The KDD conference is regarded as the premier conference for applied data science. The journal Data Mining and Knowledge Discovery is the primary research journal of the field. Work fast with our official CLI. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20% Overall Acceptance Rate 1,907 of 12,895 submissions, 15% This acceptance rate is slightly lower than those of other top computer science conferences, which typically have a rate of 15–25%. download the GitHub extension for Visual Studio, Merge remote-tracking branch 'origin/master', 2. If nothing happens, download Xcode and try again. 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18), London, United Kingdom. a concise checklist by Prof. Eamonn Keogh (UC Riverside). Introduction: SIGKDD aims to provide the premier forum for advancement and adoption of the "science" of knowledge discovery and data mining.SIGKDD will encourage: basic research in KDD (through annual research conferences, newsletter and other … We solicit submissions of papers describing designs and implementations of solutions and systems for practical tasks in data mining, data analytics, data science, and applied machine learning. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Nice, France. 1 ACL. I vote and argue for acceptance, clearly belongs in the conference. Hi! KDD 2019, ADS Track (acceptance rate: 20.7%) Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng. (Acceptance rate: 85/396=21.5%, applied data science track) Fei Wu*, Pranay Anchuri, and Zhenhui Li, Structural Event Detection from Log Messages, in Proceedings of the 2017 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17), Halifax, Nova Scotia, Aug. 2017. (acceptance rate 21%) PDF . ZIWEI ZHANG. COVID-19 J. Wang, X. Lin, Y. Liu, Qilegeri, K. Feng and H. Lin, “A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'20), Virtual Conference, 2020.8.23-2020.8.27. If you have a question that requires immediate attention, please feel free to contact us. upon methodologies and applications for extracting useful knowledge from data [1]. Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. 14 Workshops. KDD 2018 (acceptance rate of research track long presentation: 10.9%) D. Zhou, J. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. (Acceptance rate: 131/748=17.5%, research track) Please take a look at the updates below and share them with your friends and colleagues. As we get closer to the conference, we will provide a weekly recap of key announcements and program updates. (acceptance rate 21%) PDF; Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. 6: A very good paper, should be accepted. Start a SIGKDD chapter in 4 easy steps. E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. Tips for Doing Good DM Research & Get it Published! Thank you! Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. 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